Advances and increased availability of surveillance technology over the past few decades have made it increasingly common to capture and store video footage of retail settings for the protection of companies, as well as for the security and protection of employees and customers. This data has also been of interest to retail markets for its potential for data-mining and estimating consumer behavior and experience to aid both real-time decision making and historical analysis. For some large companies, slight improvements in efficiency or customer experience can have a large financial impact.
Several efforts have been made at developing retail-setting applications for surveillance video beyond well-known security and safety applications. For example, one such application counts detected people and records the count according to the direction of movement of the people. In other applications, vision equipment is used to monitor queues, and/or groups of people within queues. Still other applications attempt to monitor various behaviors within a reception setting.
One industry that is particularly heavily data-driven is fast food restaurants. Accordingly, fast food companies and/or other restaurant businesses tend to have a strong interest in numerous customer and/or store qualities and metrics that affect customer experience, such as dining area cleanliness, table usage, queue lengths, experience time in-store and drive-thru, specific order timing, order accuracy, and customer response.
Of particular interest is the detection and diagnosing of abnormal events, particularly those related to customer volumes exceeding the capacity of a store. Such events include queue lengths and waiting times exceeding certain desired thresholds, which may in turn lead to customer dissatisfaction, customer drive-offs (in vehicular queues) and walk-offs (in pedestrian queues).
In addition to losing the sale to a particular customer, drive-offs or walk-offs from a queue for any reason complicates processing of orders and leads to mistakes and slowed performance, as well as potential losses in repeat business. There is currently no suitable automated solution to the detection of these events, since current solutions for operations analytics involve manual annotation often carried out by contractors; furthermore, other events of interest may not currently be detected at all.